Application of swarm intelligence algorithms in solving the inverse heat conduction problem
Journal Title: Computer Assisted Methods in Engineering and Science - Year 2012, Vol 19, Issue 4
Abstract
In the paper a proposal of using selected swarm intelligence algorithms for solving the inverse heat conduction problem is presented. The analyzed problem consists in reconstructing temperature distribution in the given domain and the form of heat transfer coefficient appearing in the boundary condition of the third kind. The investigated approaches are based on the Artificial Bee Colony algorithm and the Ant Colony Optimization algorithm, the efficiency of which are examined and compared.
Authors and Affiliations
Edyta Hetmaniok, Damian Słota, Adam Zielonka
Keywords
Related Articles
- EP ID EP73960
- DOI -
- Views 183
- Downloads 0
Neural networks in the advisory system for repairs of industrial concrete floors
An advisory system for repairs of industrial concrete floors is a supporting tool for making material and technological decisions in the sphere of problems of recurrent character. The presented advisory system has the ch...
MPCA for flight dynamics parameters determination
Aircraft have become increasingly costly and complex. Military and civil pilots and engineers have used flight simulators in order to increase safety of flight through the training of crew. It is necessary to calibrate t...
A mixed, scalable domain decomposition method for incompressible flow
This work deals with the construction of a mixed and extensible domain decomposition method for incompressible flows. In the scheme proposed here, the solution is sought at the intersection of two spaces, one containing...
Fuzzy weight neural network in the analysis of concrete specimens and R/C column buckling tests
The paper describes the applications of back propagation neural networks with the ability to process input and output variables expressed as fuzzy numbers. The presentation of an algorithm for finding fuzzy neural networ...
Bayesian neural networks and Gaussian processes in identification of concrete properties
This paper gives a concise overview of concrete properties prediction using advanced nonlinear regression approach and Bayesian inference. Feed-forward layered neural network (FLNN) with Markov chain Monte Carlo stochast...